Objective To conduct a systematic review on the Electrocardiogram (ECG) changes in the workers exposed to high temperatures by means of meta-analysis.Methods The retrospective cohort studies on the relationship between high temperature and ECG abnormalities published from 1990 to May 2009 were searched in CNKI, VIP, WanFang database and CBM database. The literatures meeting the inclusive criteria were selected, the quality was assessed, the data were extracted, and the meta-analyses were conducted with RevMan 4.2.2 software. Results A total of 20 studies were included. The results of meta-analyses showed: the ECG abnormality rate of the high-temperature group was obviously superior to that of the control group with significant difference (OR=2.76, 95%CI 2.37 to 3.20, Plt;0.000 01). The high-temperature severely affected left ventricular hypertrophy (OR=3.49, 95%CI 2.83 to 4.31, Plt;0.000 01), sinus bradycardia (OR=2.83, 95%CI 2.33 to 3.43, Plt;0.000 01), and changes in ST-T segment (OR=2.63, 95%CI 1.48 to 4.68, P=0.000 10), which indicated that the abnormal changes of ECG, such as left ventricular hypertrophy, sinus tachycardia, sinus bradycardia, and changes in ST-T segment could be the sensitive indexes to monitor cardiovascular disease of workers exposed to high-temperature. Conclusion The incidence of ECG abnormalities caused by high-temperature operation is obviously superior to that of the control group, so it is required to strengthen the health monitoring and labor protection for the workers exposed to high temperature.
ObjectiveTo find the relationship between bicuspid aortic valve (BAV) and the dilatation or aneurysm of the aorta using electrocardiogram-gated computed tomography angiography (CTA). MethodsWe collected the clinical data of the BAV coexisting with suspected aortic dilatation or aneurysm from February 2012 through April 2015. A total of 124 patients were analyzed retrospectively. There were 97 males and 27 females at an anverage age of 50.35±16.26 years. According to the CTA, patients were classified into two groups: a pure BAV(without raphe) group and a BAV (with raphe) group. we recorded the aortic diameters, gender, age, and so on. ResultsOf the 124 patients, 91 (73.4%) had BAV with raphe, and 33 patients (26.6%) had pure BAV. The analysis revealed that the diameter of the annulus (23.90±3.34 mm vs. 21.74±3.46 mm, P=0.005), the sinuses of Valsalva (40.93±6.78 mm vs. 37.35±7.06 mm, P=0.022), the tubular portion of the ascending aorta (45.38±7.66 mm vs. 38.29±8.18 mm, P=0.0001), and the part of the aorta proximal to the innominate artery (34.19±4.98 mm vs. 30.23±6.62 mm, P=0.02) between patients with BAV with raphe and pure BAV had significant differences. And there was a significant difference in prevalence of dilatation of the aorta between patients with pure BAV and BAV with raphe [77/91 (84.6%) vs.18/31(58.1%), P=0.004]. Of the 91 BAV with raphe patients, we found 76 patients (83.5%) with right and left coronary cusps (R-L) fusion, 13 patients (14.3%) with right and non-coronary cusps (R-N) fusion, and 2 patients (1.2%) with left and non-coronary cusps (L-N) fusion. There was a statistical difference in the aortic root diameters between R-L fusion BAV and R-N fusion BAV. The diameter of the distal ascending aorta and proximal aortic arch between R-L and R-N fusion BAV had statistical differences. ConclusionsBAV with raphe is more common than pure BAV and is more often associated with dilatation and aneurysm of the ascending aorta. Otherwise R-L fusion BAV is associated with increased diameters of the aortic root, while R-N fusion BAV is associated with increased diameters of the distal ascending aorta and proximal arch.
Automated detection of myocardial infarction (MI) is crucial for preventing sudden cardiac death and enabling early intervention in cardiovascular diseases. This paper proposes a deep learning framework based on a lightweight convolutional neural network (CNN) combined with one-dimensional gradient-weighted class activation mapping (1D Grad-CAM) for the automated detection of MI and the visualization of key waveform features in single-lead electrocardiograms (ECGs). The proposed method was evaluated using a total of 432 records from the Physikalisch-Technische Bundesanstalt Diagnostic ECG Database (PTBDB) and the Normal Sinus Rhythm Database (NSRDB), comprising 334 MI and 98 normal ECGs. Experimental results demonstrated that the model achieved an accuracy, sensitivity, and specificity of 95.75%, 96.03%, and 95.47%, respectively, in MI detection. Furthermore, the visualization results indicated that the model’s decision-making process aligned closely with clinically critical features, including pathological Q waves, ST-segment elevation, and T-wave inversion. This study confirms that the proposed deep learning algorithm combined with explainable technology performs effectively in the intelligent diagnosis of MI and the visualization of critical ECG waveforms, demonstrating its potential as a useful tool for early MI risk assessment and computer-aided diagnosis.
The judgment of the type of arrhythmia is the key to the prevention and diagnosis of early cardiovascular disease. Therefore, electrocardiogram (ECG) analysis has been widely used as an important basis for doctors to diagnose. However, due to the large differences in ECG signal morphology among different patients and the unbalanced distribution of categories, the existing automatic detection algorithms for arrhythmias have certain difficulties in the identification process. This paper designs a variable scale fusion network model for automatic recognition of heart rhythm types. In this study, a variable-scale fusion network model was proposed for automatic identification of heart rhythm types. The improved ECG generation network (EGAN) module was used to solve the imbalance of ECG data, and the ECG signal was reproduced in two dimensions in the form of gray recurrence plot (GRP) and spectrogram. Combined with the branching structure of the model, the automatic classification of variable-length heart beats was realized. The results of the study were verified by the Massachusetts institute of technology and Beth Israel hospital (MIT-BIH) arrhythmia database, which distinguished eight heart rhythm types. The average accuracy rate reached 99.36%, and the sensitivity and specificity were 96.11% and 99.84%, respectively. In conclusion, it is expected that this method can be used for clinical auxiliary diagnosis and smart wearable devices in the future.
ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.
Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.
Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F1-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can identify the state of mental fatigue, which helps to maintain a healthy life. The method proposed in this paper is a new recognition method of psychological fatigue state of electrocardiogram signals based on convolutional neural network and long short-term memory. Firstly, the convolution layer of one-dimensional convolutional neural network model is used to extract local features, the key information is extracted through pooling layer, and some redundant data is removed. Then, the extracted features are used as input to the long short-term memory model to further fuse the ECG features. Finally, by integrating the key information through the full connection layer, the accurate recognition of mental fatigue state is successfully realized. The results show that compared with traditional machine learning algorithms, the proposed method significantly improves the accuracy of mental fatigue recognition to 96.3%, which provides a reliable basis for the early warning and evaluation of mental fatigue.
ObjectiveTo find out the influencing factors of electrocardiogram (ECG) monitor configuration decision in surgical nursing units and form a scientific configuration standard, so as to provide a basis for the reasonable configuration of ECG monitors.MethodsFrom May to June 2018, the indexes and weights affecting the configuration of ECG monitors in surgical nursing units of a large public hospital were determined by interview survey method and analytic hierarchy process.ResultsThe influencing factors for configuration of ECG monitors in surgical nursing units were the number of operations, number of rescues, number of emergencies, number of deaths, and number of patients transferred to and out of intensive care unit, and the weights were 0.459 7, 0.224 9, 0.155 3, 0.111 2, and 0.049 0, respectively. The classification of nursing units was taken as plan, and the configuration standard of ECG monitors was established.ConclusionThe configuration model of ECG monitors in surgical nursing units based on analytic hierarchy process realizes the combination of qualitative and quantitative analysis, which provides scientific and reasonable reference for the configuration of ECG monitors.